There are numerous execution engines used to process analytic flows. These engines may only accept input flows written in a certain programming language (e.g., PigLatin, Structured Query Language (SQL)) or designed using a certain flow-design tool (e.g., Pentaho Data Integration (PDI) platform). Furthermore, even data analytic engines supporting the same programming language or flow-design tool may provide different implementations of analytic operations and the like, and thus may have engine-specific requirements for an input flow. In today's heterogeneous analytic environments, it can be difficult to manage analytic flows due to these limitations, especially if the analytic flow is a hybrid flow comprising sub-flows directed to different engines.